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This study develops information based on index, and termed hydro-ecological-index, to represent the need of a riverine ecosystem characterized through a biologically relevant flow regime. The flow regime is defined by a set of parameters, called Indicators of Hydrologic Alteration. These parameters are predicted at the catchment scale by a hydrologic model, called Soil and Water Assessment Tool. Then the Maximum Entropy Ordered Weighted Averaging method is employed to aggregate non-commensurable biologically relevant flow regimes to develop hydro-ecological- index at the catchment scale. The resulting index reflects the variability of the need of the riverine ecosystem at catchment scale and thus different catchments can be evaluated and compared.

One of the objectives of sustainable management of water resources is to meet human needs for freshwater, while maintaining biological diversity, and hydrological and ecological processes essential for the sustenance of the composition, structure, and function of the natural environment that supports life. With the increasing concern on riverine ecosystem, many environmental flow assessment methods, such as hydrological rules, hydraulic rating methods, habitat simulation methods, and holistic methodologies [

It is now recognized that the full range of natural intra- and inter-annual variation of hydrological regimes are critical for sustaining the full native biodiversity and integrity of riverine ecosystems (the “natural flow-regime paradigm”: Indicators of Hydrologic Alteration [

Amongst the five groups, group-1 includes 12 parameters, and each parameter measures the central tendency (mean) of the daily water conditions for a given month. The 10 parameters in group-2 measure the magnitude of extreme (minimum and maximum) annual water conditions of various durations, ranging from daily to seasonal. Group-3 includes two parameters, the timing of the highest and lowest water conditions within annual cycles. The group-4 parameters include two which measure the number of annual occurrences during which the magnitude of the water condition exceeds an upper threshold or remains below a lower threshold, and two which measure the mean duration of such high and low pulses. The four parameters included in group-5 measure the number and mean rate of both positive and negative changes in water conditions from one day to the next.

Although references [

Group | Regime Characteristics | 32 Parameters |
---|---|---|

Group 1:Magnitude of monthly water conditions | Magnitude Timing | Mean value for each calendar month |

Group 2:Magnitude and duration of annual extreme water conditions | Magnitude Duration | Annual min/max of 1 day means Annual min/max of 3 day means Annual min/max of 7 day means Annual min/max of 30 day means Annual min/max of 90 day means |

Group 3:Timing of annual extreme water conditions | Timing | Julian date of each annual 1 day minimum and maximum |

Group 4:Frequency and duration of high and low pulses | Frequency Duration | Number of high and low pulses each year Mean duration of high and low pulses |

Group 5: Rate/frequency of consecutive water-condition changes | Rates of Change | Means of all positive differences between daily values Means of all negative differences between daily values Number of rises Number of falls |

On the other hand, flows are often extremely variable spatially. Flows at locations just a few kilometers apart are sometimes found to be quite different. Therefore, contemporary efforts in planning, designing and implementing resource management are at the catchment scale. Having said this, oftentimes the IHA approach is applied at a gage site [

In recent decades many mathematical models have been developed to understand the hydrological system and provide the simulated data at catchment scale that otherwise would not be measurable [

SWAT is a river basin or watershed scale model developed by the United States Department of Agriculture(USDA)―Agricultural Research Service(ARS) to predict the impact of land-management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use and management conditions over long periods of time [

The study area is Kings Creek, a tributary of the Cedar Creek River basin (^{2} as delineated from a U.S. Geological Survey (USGS) streamflow gaging station (32.513 N, 96.3286 W). Its elevation ranges from 107 m to 190 m and its land use is mainly hay (34%) and range (34.5%). The remaining areas were composed of agricultural, forest-deciduous, etc. The average annual precipitation in the study area is 1975 mm.

The SWAT model was initially set up using the ArcSWAT interface to SWAT. SWAT model input data for topography were extracted from a digital elevation model (DEM). The 30m DEM used in delineating the watersheds was taken from the NHDPlus dataset, an integrated suite of application-ready geospatial data products envisioned by the US Environmental Protection Agency. The observed daily streamflow data used in calibrating SWAT were obtained from the USGS National Water Information System (NWIS). The study area was set up to run on a daily time step. The catchments were delineated with a threshold size of 1000 hectares, resulting in 27 subbwatersheds. 10% - 20% - 10% threshold level on HRU delineation resulted in 120 HRUs for the study area. Surface runoff was calculated using the SCS curve number method. The Penman-Monteith method was used to determine potential evapotranspiration. Channel water routing was performed using the Muskingum routing method. Combination of manual and automatic calibration method was used for the calibration of SWAT model using the measured stream flow data at (32.513 N, 96.3286 W). For this analysis twenty one years, from 01 January 1963 to 31 December 1983, of meteorological and hydrometric flow data were utilized, including two years of “warm-up” period.

The objective function used in SWAT was to minimize the average Root Mean Square Error (RMSE) of the observed vs. simulated flows. The RMSE was defined as:

where n is the number of time steps, Q_{obs,i} is the observed streamflow at time i, and Q_{sim,i} is the simulated streamflow at time i. The Nash-Sutcliffe model efficiency (NSE) was used to evaluate SWAT’s overall performance during calibration and validation. The NSE was defined as:

After many trials, a good agreement between observed and simulated flows was obtained as indicated by the NSE of 0.86.

Having calibrated and validated the SWAT, all 32 IHA parameters were determined at the subwatershed level for the 21 year period (1963-1983). The SWAT prediction for each subwatershed was extracted, formatted and then coupled with IHA approach to determine all 32 IHA parameters. This can be seen as repetitive execution of IHA with multiple flow gages.

It was hypothesized that each of the 32 biologically relevant hydrologic parameters, proposed by reference [

in accord with the Principle of Maximum Entropy (POME), subject to known constraints. In Equation (3) E is the Shannon entropy,

where C_{j} is the j-th constraint, m is the number of constraints, and

In practical applications, functions

To illustrate the method, consider the condition at one of the catchment outlets for one of the IHA parameters, mean flow for the month of January, shown in _{0}, λ_{1}, and λ_{2} [1.1494, −0.9405, 0.6337]. Substitution of these values in Equation (6) yields the normal probability distribution:

In this manner, the least-biased probability distributions were determined for IHA parameters for all the catchments. For most of the parameters, the first two moments and hence the normal probability distribution sufficed, providing the maximum entropy values. For group-2 parameters which define the magnitude and duration of the ecosystem, the constraints were specified to log-normal distribution.

The maximum entropy values were computed from Equation (3) using the least biased probability distributions derived in section 4.3 for each of the biologically relevant parameters and for all the catchments. At the considered catchment outlet, the insertion of the probability distribution in Equation (3) gives the maximum entropy of 1.3006 for the mean flow for the month of January. As shown in

Thus far, it has been shown how to encapsulate the information hidden within each of the 32 biological parameters through entropy measures. This yields an array

Hydro-ecological-index was computed using the steps shown in

between minimum and maximum values of input parameters. The OWA operator is defined as

where the computed value of entropy for each of the 32 parameters is the argument (a_{i}), b_{j} is the jth largest of a_{i}, and w_{j} are a collection of weights such that w_{j} ?[0,1] and

The methodology used for obtaining the OWA weighting vector was based on [

maximize:

subject to:

The Orness characterizes the degree to which the aggregation is like an “OR” operator. For the analysis an Orness value of 0.75 was assumed in this study to ensure that the impact of all the IHA parameters is considered in the index development and to avoid assigning equal weights as some of the parameters may have more influence on defining the underlying ecosystem. Then, an array of weights w_{j} was generated using Equations (10) and (11). Using Equation (9), the hydro-ecological-index was found to be [0.3508] for the subwatershed/catch- ment in question. This procedure was followed for the other catchments as well.

To present the results in a concise way, the eco need of the study area was divided into three categories. As shown in

1) This study outlines an information based on index development to show the eco status of a river basin at catchment scale. It is shown how a hydrological model like SWAT can be used to get an insight about the riverine ecosystem through natural flow-regime paradigm: Indicators of Hydrologic Alteration. The relative values of the index clearly distinguish the catchments in terms of their needs to sustain the riverine ecosystem. This kind of analysis is significant specifically in ungaged river basins which are dominant in developing countries to define policy towards sustainability.

2) The outlined index development can be extended to analyze the impact associated with river basin activities such as water development projects (e.g., reservoirs, downstream effect of upstream development) and hypothetical climate change scenarios. In other words, instead of aggregating the entropy measure at catchment scale, one can aggregate the deviation on entropy measure at catchment scale to reflect the harness level of the system. Such analysis can provide a first insight to spot the critical locations within a river basin.

3) For this study, an Orness value of “0.75” is used to develop the hydro-ecological-index. Even though the overall evaluation and prioritization of the 32 parameters should be based on local concerns and local watershed

management goals and objectives, there is a need to evaluate the sensitivity of Orness value on the result.

4) Though the objective of the study is to show how a hydrological model like SWAT can be used to get an insight about the riverine ecosystem through natural flow-regime paradigm: Indicators of Hydrologic Alteration, at catchment scale, it is equally important to have a field reconnaissance and/or monitoring to validate the results.

The authors would like to thank Texas Water Resources Institute and U.S. Geological Survey for providing the financial support to conduct this research.

SivarajahMylevaganam,RaghavanSrinivasan,Vijay P.Singh, (2015) Ecohydrologically Driven Catchment Evaluation and Prioritization. Open Journal of Applied Sciences,05,325-334. doi: 10.4236/ojapps.2015.57033